Method of selecting an optimal propagated base signal using artificial neural networks

ABSTRACT

A system and method of propagating signal links by using artificial neural networks and a relay link selection protocol to predict an optimal signal path. The artificial neural networks used in the method classify training and testing datasets into sufficient signal strengths and insufficient signal strengths, such that paths are evaluated for predicted propagation links, such that the strongest propagation link can be selected. Specifically, a multilayer perceptron method is used to identify and characterize new link candidates using the path loss parameter or the received signal strength, such that optimal links can be selected and updated.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of and claims priority to U.S. Pat.No. 11,546,070 entitled, “Method of Selecting an Optimal Propagated BaseSignal Using Artificial Neural Networks, filed on Apr. 30, 2021, andissued Jan. 3, 2023, which claims priority to U.S. Provisional PatentApplication No. 63/018,906, entitled “Method of selecting an optimalpropagated base signal using artificial neural networks,” filed on May1, 2020, by the same inventor.

BACKGROUND OF THE INVENTION 1. Field of the Invention

This invention relates, generally, to methods of selecting a relay foroptimal wireless signal transmission with reduced fading. Morespecifically, it relates to the use of artificial neural networks andother machine learning techniques to predict signal paths, classify thepredicted signal paths, and select optimal signals to reduce signal lossand fading.

2. Brief Description of the Prior Art

Relay selection to optimize communication via wireless signals hasbecome a critical technology, particularly with 5G new radio (5G-NR) andfuture mobile communication protocols. [1]. For example, relay selectionin multi-hop communications has previously been shown to be successfulfor mobile communication over mmWave frequency bands betweenapproximately 30 GHz and 300 GHz that can be used in 5G-NRimplementations. [2], [3]. While mmWave frequencies provide fastcommunications when connected, the small bands suffer from signal pathobstacles, such that the waves are traditionally limited to use overshort distances. As such, the use of mmWave frequencies can bechallenging, and often requires the use of intermediary signal relays.However, the selection of the optimal relay and optimal signal pathdetermines the success of the signal consistency, requiring detailedanalyses of the potential signal paths before selecting a path. Previousworks have proposed adaptive multi-state selections utilizing differentmmWave frequencies. [4].

As shown in FIG. 1 , different devices may be used in different signalpathways from base station 10 to destination device 16, depending onenvironmental conditions along the path between base station 10 anddestination device 16. For example, base station 10 and destinationdevice 16 can directly transmit signals therebetween if line-of-site(LOS) is accomplished between station 10 and device 16 (labeled asline-of-site path 20 in FIG. 1 ). However, if LOS cannot beaccomplished, a second transmitted signal including a handover to relaystation 12 may be used, with the signal from base station 10 to relaystation 12 labeled as relay path 40 a, and the signal from relay station12 to destination device 16 labeled as relay path 40 b. Moreover, in theevent of an obstacle between base station 10 and destination device 16,such as obstacle 14, base station 10 may transmit a first signal toobstacle 14 (labeled as obstructed path 30 a in FIG. 1 ), with a secondsignal transmitted on an opposing side of obstacle 14 from obstacle 14to destination device (labeled as obstructed path 30 b in FIG. 1 ).

As the current state of the art signal type, 5G attempts to prioritizesignal selection based on three pillars: enhanced mobile broadband(eMBB); ultra-reliable, low latency communications (URLLC); and massivemachine type communications (mMTC). Reliability is key in successful 5Gcommunications; as such, 5G-NR requires the propagation signal strengthto attain 99.999% reliability. [5]. To accomplish the goals of 5G-NRtechnologies, previous works have suggested using deep learning toidentify and classify modulation nodes, thereby improving interferencealignment and locating an optimal routing path. [6]. Moreover, machinelearning techniques, including deep neural networks (DNN), can reducecomplexities and improve performance relating to signal path selection.[7]. However, previous works suffer from high rates of false positivesand low rates of true positives, necessitating the use of improvedmachine learning techniques to provide successful signal strengthpredictions.

Accordingly, what is needed is an artificial neural network (ANN), suchas a multilayer perceptron (MLP) model, used to classify signal pathsfor relay selection, with results that improve upon prior arttechniques. However, in view of the art considered as a whole at thetime the present invention was made, it was not obvious to those ofordinary skill in the field of this invention how the shortcomings ofthe prior art could be overcome.

While certain aspects of conventional technologies have been discussedto facilitate disclosure of the invention, Applicant in no way disclaimsthese technical aspects, and it is contemplated that the claimedinvention may encompass one or more of the conventional technicalaspects discussed herein.

The present invention may address one or more of the problems anddeficiencies of the prior art discussed above. However, it iscontemplated that the invention may prove useful in addressing otherproblems and deficiencies in a number of technical areas. Therefore, theclaimed invention should not necessarily be construed as limited toaddressing any of the particular problems or deficiencies discussedherein.

In this specification, where a document, act or item of knowledge isreferred to or discussed, this reference or discussion is not anadmission that the document, act or item of knowledge or any combinationthereof was at the priority date, publicly available, known to thepublic, part of common general knowledge, or otherwise constitutes priorart under the applicable statutory provisions; or is known to berelevant to an attempt to solve any problem with which thisspecification is concerned.

SUMMARY OF THE INVENTION

The long-standing but heretofore unfulfilled need for a method ofselecting an optimal propagated base signal using artificial neuralnetworks is now met by a new, useful, and nonobvious invention.

The novel method includes a step of building a network located on aserver, with the network including a plurality of multilayerperceptrons. In an embodiment, the server is disposed proximate to abase station. Each multilayer perceptron includes a plurality of layers,and each of the plurality of layers includes a plurality of nodes, witheach of the plurality of nodes in a single layer being connected to eachof the plurality of nodes in a remainder of the plurality of layers. Aplurality of propagation signals transmitted by a base station andreceived by a destination device are analyzed via the network to predictan optimal signal path. As such, the network models each of theplurality of propagation signals, with each of the plurality ofpropagation signals having a different associated signal path. In anembodiment, each of the plurality of propagation signals transmitted bythe base station resides within frequency ranges associated with 5G newradio standards.

The network analyzes each of the plurality of propagation signals tomeasure a sufficiency of each propagation signal based on a thresholdenergy strength. Specifically, the network measures a path loss for eachof the plurality of propagation signals. The path loss is based on afrequency of each of the plurality of propagation signals and a distancetraveled by each of the plurality of propagation signals. The networkclassifies each of the plurality of propagation signals based on abinary classification of a strong signal value and a weak signal value,such that the strong signal value includes an associated value greaterthan the threshold energy strength, and such that the weak signal valueincludes an associated value less than the threshold energy strength.The network selects the optimal signal path from the plurality ofpropagation signals, such that the optimal signal path is associatedwith the strong signal value. The base station propagates a signal tofollow the optimal signal path from the base station to the destinationdevice, such that the destination device receives the signal from thebase station.

In an embodiment, each of the plurality of layers of the multilayerperceptrons includes a plurality of hidden layers between an input layerthat receives a set of parameters and an output layer that provides aresult. In an embodiment, five hidden layers are included within eachmultilayer perceptron. The five hidden layers include a first hiddenlayer having ten neurons, a second hidden layer having fifty neurons, athird hidden layer having one-hundred neurons, a fourth hidden layerhaving fifty neurons, and a fifth hidden layer having ten neurons.

In an embodiment, the optimal signal path is a first optimal signalpath. The method includes a step of, after propagating the signal tofollow the first optimal signal path, via the network, reanalyzing theplurality of propagation signals transmitted by the base station andreceived by the destination device to predict a second optimal signalpath. Based on a determination that the second optimal signal pathdiffers from the first optimal signal path by having a greaterassociated signal value, the network selects the second optimal signalpath from the plurality of propagation signals. The base stationpropagates a signal to follow the second optimal signal path from thebase station to the destination device, such that the destination devicereceives the signal from the base station.

An object of the invention is to provide an efficient method forclassifying signal strengths and selecting signal paths, particularlyfor mmWave frequencies and other 5G-NR signals, that comports with 5G-NRreliability requirements.

These and other important objects, advantages, and features of theinvention will become clear as this disclosure proceeds.

The invention accordingly comprises the features of construction,combination of elements, and arrangement of parts that will beexemplified in the disclosure set forth hereinafter and the scope of theinvention will be indicated in the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

For a fuller understanding of the invention, reference should be made tothe following detailed description, taken in connection with theaccompanying drawings, in which:

FIG. 1 depicts three paths of signals from a base station to adestination device; an artificial neural network is used herein toselect the optimal signal path and handover when the optimal pathchanges, in accordance with an embodiment of the present invention.

FIG. 2 is a process flow diagram depicting a method of selecting anoptimal signal path from a plurality of possible signal paths, inaccordance with an embodiment of the present invention.

FIG. 3 is a graphical representation of a receiver operatingcharacteristic analysis used to compare the classification techniques ofdifferent models, in accordance with an embodiment of the presentinvention.

FIG. 4 is a graphical representation of a relay selectionprecision-recall curve used to compare the classification techniques ofdifferent models, in accordance with an embodiment of the presentinvention.

FIG. 5 graphically depicts the relationship between loss and iterationsof different models, in accordance with an embodiment of the presentinvention.

DETAILED DESCRIPTION OF THE INVENTION

In the following detailed description of the preferred embodiments,reference is made to the accompanying drawings, which form a partthereof, and within which are shown by way of illustration specificembodiments by which the invention may be practiced. It is to beunderstood that other embodiments may be utilized, and structuralchanges may be made without departing from the scope of the invention.

As used in this specification and the appended claims, the singularforms “a,” “an,” and “the” include plural referents unless the contentclearly dictates otherwise. As used in this specification and theappended claims, the term “or” is generally employed in its senseincluding “and/or” unless the context clearly dictates otherwise.

The present invention includes a mechanism to overcome the problemsassociated with signal propagation links by using artificial neuralnetworks (ANN). Using a relay link selection protocol, it was found thatmachine learning can be used to describe and predict an optimal link orpath, providing a reliable mechanism to meet 5G-NR's requirements oftrustworthy communications under uRLLC and enhanced coverage andimproved communications under eMBB. The supervised classificationalgorithms of the ANN provide categorical class labels when training thedataset of an outdoor urban environment, such that paths are evaluatedfor predicted propagation links, and such that the strongest propagationlink can be selected. Specifically, the multilayer perceptron (MLP)method provides the classifications in the ANN and is used to identifyand characterize new link candidates using the path loss parameter orthe received signal strength, such that optimal links can be selected.The MLP and ANN methods can also be used to improve massive machine typecommunications (mMTC). The links that are classified by the system arethe incoming signals, and optimum signals are selected based on theclassified incoming signal strength. Moreover, based on a data-drivenfeed-forward neural network-based system using back-propagation andweight adjustment, the MLP reduces false positives and increases truepositives relating to a prediction of the optimum signal strengthbetween a transmitter and a receiver, thereby enhancing predictionaccuracy. The systems and methods of signal selection will be discussedin greater detail herein below.

Referring again to FIG. 1 , a signal propagation system is shown ingreater detail. While signal propagation systems are generally knownwithin the prior art, as noted above, the present invention includes asignal path selection protocol that utilizes ANNs, and particularly,MLPs to determine an optimal signal path in real-time. As such, FIG. 1depicts an example of three different signal propagation paths that arepossible in the practice of the methods described herein. Accordingly,as shown in FIG. 1 , base station 10 is in wireless electroniccommunication with destination device 16, such that signals can bewirelessly transmitted to and received by each of base station 10 anddestination device 16. As used herein, a base station is a physicaldevice, such as a cellular-enabled tower, radio mast, array, cell site,or other device that is capable of receiving and transmitting signalsacross distances to and from electronic devices, such as via antennas.As used herein, a destination device is an electronic device that isconfigured for wired or wireless communications by transmitting andreceiving signals, such as a computing device, mobile telephone, tablet,wearable device, and other similar electronic devices.

In an embodiment, the wireless electronic communication between basestation 10 and destination device 16 is accomplished via a direct linkbetween the devices, accomplished via line-of-sight path 20 betweenstation 10 and device 16. As such, base station 10 and destinationdevice 16 can communicate directly with few or no obstacles obstructingsignals propagated by either base station 10 or destination device 16.In some embodiments, line-of-sight path 20 provides an optimal signalpath between base station 10 and destination device 16, providing thestrongest signal propagation between station 10 and device 16.

However, as shown in FIG. 1 , line-of-sight path 20 may not be possiblein all situations; alternatively, line-of-sight path 20 may suffer froman obstruction or other reason for incurring a drop in signal strength.Accordingly, alternative signal propagation paths exist between basestation 10 and destination device 16. For example, obstruction 14, suchas a building, may be disposed between base station 10 and destinationdevice 16, blocking a signal propagated by base station 10 andtransmitted in a direction toward destination device 16. In suchembodiments, base station 10 propagates a signal via a first portion ofobstructed path 30 a, such that base station 10 transmits the signal ina direction toward obstruction 14. As the signal reaches obstruction 14,the signal follows a second portion of obstructed path 30 b, such thatthe signal traverses beyond obstruction 14 and is received bydestination device 16.

Similarly, in an embodiment, relay station 12 may be disposed betweenbase station 10 and destination device 16. Relay station 12 is inwireless electronic communication with base station 10, such thatsignals propagated by base station 10 can be received and transmitted byrelay station 12. In such embodiments, a signal propagated by basestation 10 is receivable by relay station 12 by following a firstportion of relay path 40 a. After relay station 12 receives the signal,relay station 12 retransmits the signal in a direction towarddestination device 16 via a second portion of relay path 40 b.

Each of the propagation signals following the paths outlined above mustbe classified to determine a likelihood of successful signal transfer todestination device 16. The classification technique used in the ANN isbinary, including a class of a strong link and a class of a weak link,thereby maintaining a simple classification system. Once propagationresults are obtained, the ANN separates the signal losses into classesto consider whether the signal is sufficient or insufficient, accordingto the binary classification system. To determine the sufficiency of asignal, a threshold energy strength is determined (for example, themethods used herein used a threshold of −120 dBm; any energy strengthbelow the threshold is considered a poor propagation and is classifiedas an insufficient signal. However, it should be appreciated thatalternative threshold values can be used to determine signalsufficiency), as shown in Eq. 1:

$\begin{matrix}{{C_{i}(x)} = \{ \begin{matrix}{1,} & {{PL} < {120{dB}m}} \\{0,} & {{PL} \geq {120{dB}m}}\end{matrix} } & (1)\end{matrix}$

where C_(i) (x) is the link selection class, which depends on the pathloss (PL) of the link that can be calculated using models, such as aFloating-Intercept (FI) model of Eqs. 2-3:PL ^(FI)(f,d)[dB]=a+10β log ₁₀(d)+X _(σ) ^(FL)  (2)

$\begin{matrix}{{BL} = {\arg{\min\limits_{L_{s}}( {PL_{n}} )}}} & (3)\end{matrix}$

where PL^(FI)(f, d) represents path loss in dB based on frequency anddistance; a is coefficient representing an optimized offset value forpath loss in dB; β is a coefficient representing path loss dependence ondistance; X_(σ) ^(FL) represents a standard deviation for large-scalesignal fluctuations over distance; BL is the best link selection; andPL_(n) is the path loss of the propagated links, while n is the numberof transmitted links between the base station and the destinationdevice.

By applying prediction techniques using classification and clustering toestimate the channel path loss, better performance and precision can beattained. For example, MLP is a multilayer classification technique thatis a neural network. The data can be classified based on maximumprobabilities to predict path loss according to Eq. 4:

$\begin{matrix}{\overset{\hat{}}{C} = {\arg\max\limits_{i = 1}{P( {C_{i}/X} )}}} & (4)\end{matrix}$where Ĉ is the prediction path loss class, and P(C_(i)/X) is theconditional probability of dataset features given the class. MLP methodswill be described in more detail herein below.

MLP uses feed-forward neural networks (FFNNs) and back-propagationnetworks to compute losses and adjust weights [11], making MLP suitablefor deep learning. MLP forms a fully connected network in which everynode in a single layer is connected to every node in subsequent layers.The subsequent error is usually obtained by the loss function, andoptimization methods can be used to minimize loss (such as adaptivemoment estimation, or Adam, optimization algorithms, which arereplacement optimization algorithms for stochastic gradient descents fordeep learning models). There are multiples of loss functions, and crossentropy is used when relay selection is initially viewed as a binaryclassification problem. MLP is a multivariate multiple nonlinearregression and collection of neurons, serving as a classification bybuilding decisions. MLPs are typically uncorrelated, and a collection ofMLPs make up the network, making the network less prone to overfitting.MLP is mathematically expressed in Eqs. 5-7:

^(n)→

^(rn) :y ₁ ,y ₂ , . . . ,y _(n))  (5)

$\begin{matrix}{y_{n} = {g_{s}( {w_{0} + {{\sum}_{i = 1}^{n}w_{i}y_{i}}} )}} & (6)\end{matrix}$ $\begin{matrix}{y_{2} = {g_{out}( {w_{k0}^{(2)} + {{\sum}_{j = 1}^{M}w_{k0}^{(2)}{\gamma( {w_{j0}^{(1)} + {{\sum}_{i = 1}^{n}w_{ji}^{(1)}y_{i}}} )}}} )}} & (7)\end{matrix}$

wherein represents the real number of independent and dependent (y₁, y₂,. . . , y_(n)) data samples. The above structure proceeds with only twolayers, in which y₀=1 as the output of the first layer. g_(s) is theactivation function and is expressed in Eqs. 8-9:g(·):R→R  (8)

$\begin{matrix}{{g_{s}(x)} = \{ \begin{matrix}{0,} & {x < 0} \\{1,} & {x \geq 0}\end{matrix} } & (9)\end{matrix}$

To generate the reliability required under 5G-NR, Adam optimization isused to update the weight iterative base in the training data [13] witha learning rate or step size a.

Artificial intelligence (AI), particularly machine learning (ML),enables a system to learn, predict, and assess data without the need forhuman involvement. [14]. A main problem in current communicationsstandards is that of handover; however, ML can enhance predictionaccuracies and reduce complexity. Attempts have been made to utilizedifferent ML mechanisms to improve signal strength and communicationsnetworks; however, such attempts failed to produce accurate results duein part to the non-use of MLPs. [15-19, 10].

The current method improves over prior art attempts by using MLPsinstead. MLPs are typically used for both classification andregressions, with the classifications being binary or multiple, and theregressions being used for continuous outputs. However, the currentmethod classifies link strengths in binary classes to predict theoptimal link propagation and does not require the use of MLPs forregressions. MLP follows Eq. 10:

$\begin{matrix}{y = {\phi( {{{\sum}_{i = 1}^{n}w_{i}X_{i}} + b} )}} & (10)\end{matrix}$where w is the vector of weights of X vector inputs, b is the error, andϕ is the nonlinear activation function.

Referring now to FIG. 2 , in conjunction with FIG. 1 , an exemplaryprocess-flow diagram is provided, depicting a method of selecting anoptimal signal path from a plurality of possible signal paths. The stepsdelineated in the exemplary process-flow diagram of FIG. 4 are merelyexemplary of an order of selecting an optimal signal path. The steps maybe carried out in another order, with or without additional stepsincluded therein.

As described above, multiple signal paths are possible as a signalpropagated by base station 10 travels in a direction toward destinationdevice 16, which is disposed to receive the signal. Accordingly, asshown in FIG. 2 , the method of selecting an optimal signal path beginswith step 100, which includes building a network located on a server,such that the network includes a plurality of MLPs, each MLP including aplurality of layers that in turn include a plurality of interconnectednodes, as described in detail above. During step 110, the networkanalyzes a plurality of propagation signals transmitted by base station10 and received by destination device 16 to predict an optimal signalpath.

During step 120, the network analysis includes a step of modeling eachof the plurality of propagation signals, with each of the plurality ofpropagation signals having a different associated signal path andanalyzing each signal to measure signal strength based on measured pathloss. For example, in an embodiment, the network measure a sufficiencyof each propagation signal based on a threshold energy strength bymeasuring a path loss for each of the plurality of propagation signals.The path loss is based on a frequency of each of the plurality ofpropagation signals and a distance traveled by each of the plurality ofpropagation signals.

During step 130, the network classifies each of the plurality ofpropagation signals based on a binary classification of a strong signalvalue and a weak signal value. The strong signal value includes anassociated value greater than the threshold energy strength. Similarly,the weak signal value includes an associated value less than thethreshold energy strength. During step 140, the network selects theoptimal signal path from the plurality of propagation signals, such thatthe optimal signal path is associated with the strong signal value.Also, during step 140, base station 10 propagates a signal to follow theoptimal signal path from base station 10 to destination device 16, suchthat destination device 16 receives the signal from base station 10.Embodiments of the method described herein, including comparativeresults with prior art classification attempts, are described in greaterdetail in the section below.

Experimental Results

Six models of MLPs with different specifications are analyzed andcompared with traditional ML methods: Model 1 (one hidden layer of 10neurons); Model 2 (two hidden layers of 50 and 10 neurons); Model 3(three hidden layers of 10, 50, and 10 neurons); Model 4 (four hiddenlayers of 10, 50, 50, and 10 neurons); Model 5 (five hidden layers of10, 50, 100, 50, and 10 neurons); Model 6 (eight hidden layers of 10,50, 100, 100, 50, and 10 neurons); Model 7 (logistic regression model);Model 8 (dummy classifier model); and Model 9 (support vector machine).MLPs are employed to predict the optimal propagated link in the relayselection. Then, MLPs are compared with other ML techniques based onprecision, recall, F1 score, accuracy, and support parameters. Resultsare explored using simulated data showing the accuracy of applying deeplearning techniques. It is shown that MLP excels at both the predictionof link performance and the classification to select an appropriatelink, providing a method of predicting links with low path loss,providing for a reliable handover to meet the 5G-NR end needs of eMBBand uRLLC. While other ANN methods exist, such as convolutional neuralnetworks for images where 2D and 3D inputs exist, MLP excels at signalpropagation prediction and selection.

The dataset for the experiment was generated after modification usingopen source Matlab simulations by New York University. [20, 21]. Thedataset of the wireless channels includes two fragments. The selectedmodel is trained and validated on the dataset and is tested using theunseen data. In the experiment, the training portion of the dataset was75% of the set, and the testing portion of the dataset was 25% of theset. The classes of the classification are binary, with each signalbeing classified with a 1 (sufficient) or a 0 (insufficient). Themeasurements are specified based on distance from 1 m to 40 m, therebybeing suitable for frequencies from 500 MHz to 100 GHz and bandwidths upto 800 MHz. The dataset used consists of channel properties of acommunications link, such that the information helps the base stationexecute supervised classification based on datasets from priormeasurements or simulations. The parameters are shown in Table 1 below:

TABLE 1 Channel Measurement Parameters Parameter Value Distance (m) 1-40Frequency (GHz) 28 Bandwidth (MHz) 800 TXPower (dBm) 30 Scenario UMiPolarization Co-Pol Tx ArrayType ULA (transmission array) (uniformlinear array) RxArrayType (receiving array) ULA Antenna SISO (single-input single- output) Tx/Rx antenna Azimuth and 10° Elevation (red)

To accomplish a broad exploration, MLP, logical regression, dummyclassifier, and support vector machine analyses are used to performclassifications and are evaluated by confusion matrix, which quantifiesthe outcomes of prediction models compared to the training dataset.[23]. The precision parameter typically indicates how often a modelmakes a positive prediction and the recall shows the confidence level ofa model of predicting all positive targets. As noted above, accuracy,precision, recall, and F1 score metrics were used to evaluate the MLclassifiers. The accuracy parameter is a measurement of the number oftrue predictions to the total number of predictions, or the number ofcorrectly predicted selected links divided by the total number of links,indicating if the classifier is able to avoid misclassifying a positivepath loss. The precision parameter represents the number of truepositives (Tp) divided by the number of true and false positives (Fp).The recall parameter represents the number of true positives divided bythe number of true positives and false negatives (FN). Finally, the F1score measures the harmonic mean for both precision and recall. Thesevalues are expressed in Eqs. 11-13:

$\begin{matrix}{{{Average}{Precision}} = {\frac{1}{n}{\sum}_{i = 1}^{N}\frac{Tp}{{Tp} + {Fp}}}} & (11)\end{matrix}$

$\begin{matrix}{{{{Total}{Recall}} = {{\sum}_{i = 1}^{N}\frac{Tp}{{Tp} + {FN}}}}} & (12)\end{matrix}$ $\begin{matrix}{{F1{Score}} = {2 \times \frac{{precision} \times {recall}}{{precision} + {recall}}}} & (13)\end{matrix}$

Results from testing each of the nine models described above are shownin Tables 2-3 below:

TABLE 2 Interpretation of Performance Measures ANN Model PrecisionRecall F1 Score Model 1 0.39 0.61 0.47 Model 2 0.88 0.87 0.87 Model 30.86 0.86 0.86 Model 4 0.93 0.91 0.92 Model 5 0.98 0.98 0.98 Model 60.88 0.87 0.88 Logistic Regression 0.86 0.86 0.86 Dummy Classifier 0.560.57 0.57 SVM 0.92 0.93 0.93

TABLE 3 Accuracy Compression of Models ANN Model Accuracy ROC AUC ScoreModel 1 0.623 0.484 Model 2 0.868 0.877 Model 3 0.857 0.842 Model 40.925 0.932 Model 5 0.982 0.981 Model 6 0.882 0.866 Logistic Regression0.882 0.866 Dummy Classifier 0.857 0.848 SVM 0.934 0.973

As shown in Tables 2-3 above, Model 5 (five hidden layers of 10, 50,100, 50, and 10 neurons) performed best among the tested models,followed by Model 4; Model 1 and the dummy classifier model performedthe worst among the tested models. A possible explanation for theperformance is that some of the features depend on each other, such asdistance and received power. Model 6, despite having a greater number ofneurons than Model 5, began degrading once the number of hidden layersreached 70% of the number of inputs, as shown in FIG. 3 (showing thereceiver operating characteristic (ROC) curves of the classificationtechniques). Each ROC curve visually represents a classifier'sperformance by plotting the false positive rate against the truepositive rate, and the collection of ROC curves in FIG. 3 shows thatModel 5 is the optimal model, with a reliability result of approximately99%. In addition, FIG. 4 illustrates the relay selectionprecision-recall curve (the relationship between the true positive rateand the positive prediction value) for each model, again showing thatModel 5 performed the best among the tested models.

Finally, FIG. 5 shows the loss versus neural iterations curves for themodels, depicting the point at which the training data will not improvethe performance of the model by at least a tolerance value (such as1e⁻⁴) or by having a constant loss for multiple iterations. As shown inFIG. 4 , the losses of models decreased smoothly, except Model 1 (due tothe adjusted learning rate of Model 1 of 1e⁻⁵, while the other modelshave an adjusted learning rate of 0.05). Analyzing Model 6 inparticular, at iteration number 185, the curve begins to increase,indicating that the model should be stopped to avoid issues withoverfitting and decreasing the efficiency of the model. Again, FIG. 5shows that Model 5 is the optimal ANN model.

The present invention may be embodied on various computing platformsthat perform actions responsive to software-based instructions. Thefollowing provides an antecedent basis for the information technologythat may be utilized to enable the invention.

The computer readable medium described in the claims below may be acomputer readable signal medium or a computer readable storage medium. Acomputer readable storage medium may be, for example, but not limitedto, an electronic, magnetic, optical, electromagnetic, infrared, orsemiconductor system, apparatus, or device, or any suitable combinationof the foregoing. More specific examples (a non-exhaustive list) of thecomputer readable storage medium would include the following: anelectrical connection having one or more wires, a portable computerdiskette, a hard disk, a random access memory (RAM), a read-only memory(ROM), an erasable programmable read-only memory (EPROM or Flashmemory), an optical fiber, a portable compact disc read-only memory(CD-ROM), an optical storage device, a magnetic storage device, or anysuitable combination of the foregoing. In the context of this document,a computer readable storage medium may be any non-transitory, tangiblemedium that can contain, or store a program for use by or in connectionwith an instruction execution system, apparatus, or device.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including, but not limited to,electro-magnetic, optical, or any suitable combination thereof. Acomputer readable signal medium may be any computer readable medium thatis not a computer readable storage medium and that can communicate,propagate, or transport a program for use by or in connection with aninstruction execution system, apparatus, or device. However, asindicated above, due to circuit statutory subject matter restrictions,claims to this invention as a software product are those embodied in anon-transitory software medium such as a computer hard drive, flash-RAM,optical disk or the like.

Program code embodied on a computer readable medium may be transmittedusing any appropriate medium, including but not limited to wireless,wire-line, optical fiber cable, radio frequency, etc., or any suitablecombination of the foregoing. Computer program code for carrying outoperations for aspects of the present invention may be written in anycombination of one or more programming languages, including anobject-oriented programming language such as Java, C#, C++, Visual Basicor the like and conventional procedural programming languages, such asthe “C” programming language or similar programming languages.

REFERENCES

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All referenced publications are incorporated herein by reference intheir entirety. Furthermore, where a definition or use of a term in areference, which is incorporated by reference herein, is inconsistent orcontrary to the definition of that term provided herein, the definitionof that term provided herein applies and the definition of that term inthe reference does not apply.

The advantages set forth above, and those made apparent from theforegoing description, are efficiently attained. Since certain changesmay be made in the above construction without departing from the scopeof the invention, it is intended that all matters contained in theforegoing description or shown in the accompanying drawings shall beinterpreted as illustrative and not in a limiting sense.

It is also to be understood that the following claims are intended tocover all of the generic and specific features of the invention hereindescribed, and all statements of the scope of the invention that, as amatter of language, might be said to fall therebetween.

What is claimed is:
 1. A method of selecting an optimal signal pathbetween a base station and a destination device, the method comprisingthe steps of: receiving, at a destination device, a plurality ofpropagation signals transmitted by a base station, each of the pluralityof propagation signals having a different associated signal path;measuring a path loss for each propagation signal of the plurality ofpropagation signals, the path loss based on a frequency of eachpropagation signal of the plurality of propagation signals and adistance traveled by each propagation signal of the plurality ofpropagation signals; and classifying each propagation signal of theplurality of propagation signals based on a binary classification of astrong signal value and a weak signal value, wherein the strong signalvalue includes an associated value greater than a threshold energystrength, and wherein the weak signal value includes an associated valueless than the threshold energy strength; selecting an optimal signalpath for the propagation signal, wherein the optimal signal path is thesignal path associated with the propagation signal of the plurality ofpropagation signals classified having the strongest signal value;propagating, via the base station, a signal to follow the optimal signalpath from the base station to the destination device, such that thedestination device receives the signal from the base station.
 2. Themethod of claim 1, further comprising: building a network located on aserver, the network including a plurality of multilayer perceptrons,each multilayer perceptron including a plurality of layers, each of theplurality of layers including a plurality of nodes and each of theplurality of nodes in a single layer being connected to each of theplurality of nodes in a remainder of the plurality of layers and whereinthe server is located proximate to the base station.
 3. The method ofclaim 2, wherein, for each of the plurality of multilayer perceptrons,the plurality of layers includes a plurality of hidden layers between aninput layer that receives a set of parameters and an output layer thatprovides a result.
 4. The method of claim 3, wherein the plurality ofhidden layers includes five hidden layers.
 5. The method of claim 4,wherein the five hidden layers include a first hidden layer having tenneurons, a second hidden layer having fifty neurons, a third hiddenlayer having one-hundred neurons, a fourth hidden layer having fiftyneurons, and a fifth hidden layer having ten neurons.
 6. The method ofclaim 1, wherein the optimal signal path is a first optimal signal path,further comprising, after propagating the signal to follow the firstoptimal signal path, reanalyzing the plurality of propagation signalstransmitted by the base station and received by the destination deviceto predict a second optimal signal path.
 7. The method of claim 6,further comprising, based on a determination that the second optimalsignal path differs from the first optimal signal path by having agreater associated signal value, selecting the second optimal signalpath from the plurality of propagation signals.
 8. The method of claim7, further comprising the step of propagating, via the base station, asignal to follow the second optimal signal path from the base station tothe destination device, such that the destination device receives thesignal from the base station.
 9. The method of claim 1, wherein eachpropagation signal of the plurality of propagation signals transmittedby the base station has a frequency within a frequency range associatedwith 5G new radio standards.
 10. A system for selecting an optimalsignal path between a base station and a destination device, the systemcomprising: a base station spaced apart from a destination device, thebase station for transmitting a plurality of propagation signals to thedestination device, wherein each of the plurality of propagation signalshas a different associated signal path and wherein the destinationdevice is configured to; measure a path loss for each propagation signalof the plurality of propagation signals, the path loss based on afrequency of each propagation signal of the plurality of propagationsignals and a distance traveled by each propagation signal of theplurality of propagation signals; classify each propagation signal ofthe plurality of propagation signals based on a binary classification ofa strong signal value and a weak signal value, such that the strongsignal value includes an associated value greater than a thresholdenergy strength, and such that the weak signal value includes anassociated value less than the threshold energy strength; select anoptimal signal path for the propagation signal, wherein the optimalsignal path is the signal path associated with the propagation signal ofthe plurality of propagation signals classified having the strongestsignal value; and wherein the base station is configured to propagate asignal to follow the optimal signal path from the base station to thedestination device, such that the destination device receives the signalfrom the base station.
 11. The system of claim 10, wherein the basestation comprises a computing node housing a server thereon, the serverincluding a network having a plurality of multilayer perceptrons, eachmultilayer perceptron including a plurality of layers, each of theplurality of layers including a plurality of nodes, with each of theplurality of nodes in a single layer being connected to each of theplurality of nodes in a remainder of the plurality of layers; and foreach of the plurality of multilayer perceptrons, the plurality of layersincludes a plurality of hidden layers between an input layer thatreceives a set of parameters and an output layer that provides a result.12. The system of claim 11, wherein the plurality of hidden layersincludes five hidden layers.
 13. The system of claim 12, wherein thefive hidden layers include a first hidden layer having ten neurons, asecond hidden layer having fifty neurons, a third hidden layer havingone-hundred neurons, a fourth hidden layer having fifty neurons, and afifth hidden layer having ten neurons.
 14. The system of claim 10,wherein the optimal signal path is a first optimal signal path, whereinthe destination device is configured to reanalyze the plurality ofpropagation signals transmitted by the base station and received by thedestination device to predict a second optimal signal path.
 15. Thesystem of claim 14, wherein the destination device is configured toselect the second optimal signal path from the plurality of propagationsignals based on a determination that the second optimal signal pathdiffers from the first optimal signal path by having a greaterassociated signal value.
 16. The system of claim 15, wherein the basestation propagates a signal to follow the second optimal signal pathfrom the base station to the destination device, such that thedestination device receives the signal from the base station.
 17. Thesystem of claim 10, wherein each propagation signal of the plurality ofpropagation signals transmitted by the base station has a frequencywithin a frequency range associated with 5G new radio standards.
 18. Amethod of selecting an optimal signal path between a base station and adestination device, the method comprising: disposing a server proximateto a base station, the server including a network having a plurality ofmultilayer perceptrons, each multilayer perceptron including an inputlayer, an output layer, and a plurality of hidden layers disposedbetween the input layer and the output layer, each of the plurality ofhidden layers including a plurality of nodes, with each of the pluralityof nodes in a single hidden layer being connected to each of theplurality of nodes in a remainder of the plurality of hidden layers;receiving, at a destination device, a plurality of propagation signalstransmitted by the base station, each of the plurality of propagationsignals having a different associated signal path; measuring a path lossfor each propagation signal of the plurality of propagation signals, thepath loss based on a frequency of each propagation signal of theplurality of propagation signals and a distance traveled by eachpropagation signal of the plurality of propagation signals; andclassifying each propagation signal of the plurality of propagationsignals based on a binary classification of a strong signal value and aweak signal value, wherein the strong signal value includes anassociated value greater than a threshold energy strength, and whereinthe weak signal value includes an associated value less than thethreshold energy strength; selecting an optimal signal path for thepropagation signal, wherein the optimal signal path is the signal pathassociated with the propagation signal of the plurality of propagationsignals classified having the strongest signal value; propagating, viathe base station, a signal to follow the optimal signal path from thebase station to the destination device, such that the destination devicereceives the signal from the base station.
 19. The method of claim 18,wherein the optimal signal path is a first optimal signal path, furthercomprising the step of, after propagating the signal to follow the firstoptimal signal path, via the plurality of multilayer perceptrons,reanalyzing the plurality of propagation signals transmitted by the basestation and received by the destination device to predict a secondoptimal signal path.
 20. The method of claim 19, further comprising thesteps of: based on a determination that the second optimal signal pathdiffers from the first optimal signal path by having a greaterassociated signal value, selecting, via the network, the second optimalsignal path from the plurality of propagation signals; and propagating,via the base station, a signal to follow the second optimal signal pathfrom the base station to the destination device, such that thedestination device receives the signal from the base station.